language:
- en
license: apache-2.0
task_categories:
- question-answering
- image-text-to-text
configs:
- config_name: benchmark
data_files:
- split: VidoSeek
path: vidoseek.json
- split: SlideVQA_Refined
path: slidevqa_refined.json
🚀Overview
This is the Repo for ViDoSeek, a benchmark specifically designed for visually rich document retrieval-reason-answer, fully suited for evaluation of RAG within large document corpus.
- The paper is available at https://arxiv.org/abs/2502.18017.
- ViDoRAG Project: https://github.com/Alibaba-NLP/ViDoRAG
ViDoSeek sets itself apart with its heightened difficulty level, attributed to the multi-document context and the intricate nature of its content types, particularly the Layout category. The dataset contains both single-hop and multi-hop queries, presenting a diverse set of challenges. We have also released the SlideVQA-Refined dataset which is refined through our pipeline. This dataset is suitable for evaluating retrieval-augmented generation tasks as well.
🔍Dataset Format
The annotation is in the form of a JSON file.
{
"uid": "04d8bb0db929110f204723c56e5386c1d8d21587_2",
// Unique identifier to distinguish different queries
"query": "What is the temperature of Steam explosion of Pretreatment for Switchgrass and Sugarcane bagasse preparation?",
// Query content
"reference_answer": "195-205 Centigrade",
// Reference answer to the query
"meta_info": {
"file_name": "Pretreatment_of_Switchgrass.pdf",
// Original file name, typically a PDF file
"reference_page": [10, 11],
// Reference page numbers represented as an array
"source_type": "Text",
// Type of data source, 2d_layout\Text\Table\Chart
"query_type": "Multi-Hop"
// Query type, Multi-Hop or Single-Hop
}
}
📚 Download and Pre-Process
To use ViDoSeek, you need to download the document files vidoseek_pdf_document.zip and query annotations vidoseek.json.
Optionally, you can use the code we provide to process the dataset and perform inference. The process code is available at https://github.com/Alibaba-NLP/ViDoRAG/tree/main/scripts.
📝 Citation
If you find this dataset useful, please consider citing our paper:
@article{wang2025vidorag,
title={ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents},
author={Wang, Qiuchen and Ding, Ruixue and Chen, Zehui and Wu, Weiqi and Wang, Shihang and Xie, Pengjun and Zhao, Feng},
journal={arXiv preprint arXiv:2502.18017},
year={2025}
}